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Wrap-up

Dr. D’Agostino McGowan

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Dr. Lucy D'Agostino McGowan

What have we learned?

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Dr. Lucy D'Agostino McGowan

What have we learned?

  • Supervised learning techniques
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Dr. Lucy D'Agostino McGowan

What have we learned?

  • Supervised learning techniques
  • Focused on prediction
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Dr. Lucy D'Agostino McGowan

What have we learned?

  • Supervised learning techniques
  • Focused on prediction
  • bias-variance trade-off
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Dr. Lucy D'Agostino McGowan

How do we ensure that we aren't overfitting?

  • We tune using an estimate of the test error and find the model that minimizes this
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Dr. Lucy D'Agostino McGowan

How do we ensure that we aren't overfitting?

  • We tune using an estimate of the test error and find the model that minimizes this

How do we estimate the test error?

  • Cross-validation
  • Validation Set
  • A combination of both
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Dr. Lucy D'Agostino McGowan

Types of models

  • Regression
  • Classification
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Dr. Lucy D'Agostino McGowan

Regression

  • Linear Regression
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Dr. Lucy D'Agostino McGowan

Regression

  • Linear Regression
    • Ridge
    • Lasso
    • Elastic net
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Dr. Lucy D'Agostino McGowan

Regression

  • Linear Regression
    • Ridge
    • Lasso
    • Elastic net
  • Regression Trees
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Dr. Lucy D'Agostino McGowan

Regression

  • Linear Regression
    • Ridge
    • Lasso
    • Elastic net
  • Regression Trees
  • Ensemble Trees
    • Bagged Trees
    • Random Forest
    • Boosted Trees
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Dr. Lucy D'Agostino McGowan

tidymodels

library(tidymodels)
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Dr. Lucy D'Agostino McGowan

Linear Regression in R

mod_spec <- linear_reg() %>%
  set_engine("lm")
mod_spec
## Linear Regression Model Specification (regression)
## 
## Computational engine: lm
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Dr. Lucy D'Agostino McGowan

Ridge Regression in R

mod_spec <- linear_reg(penalty = 10, mixture = 0) %>%
  set_engine("glmnet")
mod_spec
## Linear Regression Model Specification (regression)
## 
## Main Arguments:
##   penalty = 10
##   mixture = 0
## 
## Computational engine: glmnet
8 / 22
Dr. Lucy D'Agostino McGowan

Lasso in R

mod_spec <- linear_reg(penalty = 10, mixture = 1) %>%
  set_engine("glmnet")
mod_spec
## Linear Regression Model Specification (regression)
## 
## Main Arguments:
##   penalty = 10
##   mixture = 1
## 
## Computational engine: glmnet
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Dr. Lucy D'Agostino McGowan

Elastic net in R

mod_spec <- linear_reg(penalty = 10, mixture = 0.5) %>%
  set_engine("glmnet")
mod_spec
## Linear Regression Model Specification (regression)
## 
## Main Arguments:
##   penalty = 10
##   mixture = 0.5
## 
## Computational engine: glmnet
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Dr. Lucy D'Agostino McGowan

Regression Trees in R

mod_spec <- decision_tree(mode = "regression") %>%
  set_engine("rpart")
mod_spec
## Decision Tree Model Specification (regression)
## 
## Computational engine: rpart
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Dr. Lucy D'Agostino McGowan

Bagging in R

mod_spec <- rand_forest(
  mode = "regression",
  mtry = 10) %>%
  set_engine("ranger")
mod_spec
## Random Forest Model Specification (regression)
## 
## Main Arguments:
##   mtry = 10
## 
## Computational engine: ranger
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Dr. Lucy D'Agostino McGowan

Random Forest in R

mod_spec <- rand_forest(
  mode = "regression") %>%
  set_engine("ranger")
mod_spec
## Random Forest Model Specification (regression)
## 
## Computational engine: ranger
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Dr. Lucy D'Agostino McGowan

Boosting in R

mod_spec <- boost_tree(
  mode = "regression") %>%
  set_engine("xgboost")
mod_spec
## Boosted Tree Model Specification (regression)
## 
## Computational engine: xgboost
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Dr. Lucy D'Agostino McGowan

Classification

  • Logistic Regression
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Dr. Lucy D'Agostino McGowan

Classification

  • Logistic Regression
    • Ridge
    • Lasso
    • Elastic net
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Dr. Lucy D'Agostino McGowan

Classification

  • Logistic Regression
    • Ridge
    • Lasso
    • Elastic net
  • Classification Trees
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Dr. Lucy D'Agostino McGowan

Classification

  • Logistic Regression
    • Ridge
    • Lasso
    • Elastic net
  • Classification Trees
  • Ensemble Trees
    • Bagged Trees
    • Random Forest
    • Boosted Trees
15 / 22
Dr. Lucy D'Agostino McGowan

Logistic Regression in R

mod_spec <- logistic_reg() %>%
  set_engine("glm")
mod_spec
## Logistic Regression Model Specification (classification)
## 
## Computational engine: glm
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Dr. Lucy D'Agostino McGowan

Ridge (Logistic) in R

mod_spec <- logistic_reg(penalty = 10, mixture = 0) %>%
  set_engine("glmnet")
mod_spec
## Logistic Regression Model Specification (classification)
## 
## Main Arguments:
##   penalty = 10
##   mixture = 0
## 
## Computational engine: glmnet
17 / 22
Dr. Lucy D'Agostino McGowan

Lasso (Logistic) in R

mod_spec <- logistic_reg(penalty = 10, mixture = 1) %>%
  set_engine("glmnet")
mod_spec
## Logistic Regression Model Specification (classification)
## 
## Main Arguments:
##   penalty = 10
##   mixture = 1
## 
## Computational engine: glmnet
18 / 22
Dr. Lucy D'Agostino McGowan

Elastic net (Logistic) in R

mod_spec <- logistic_reg(penalty = 10, mixture = 0.5) %>%
  set_engine("glmnet")
mod_spec
## Logistic Regression Model Specification (classification)
## 
## Main Arguments:
##   penalty = 10
##   mixture = 0.5
## 
## Computational engine: glmnet
19 / 22
Dr. Lucy D'Agostino McGowan

Bagging in R

mod_spec <- rand_forest(
  mode = "classification",
  mtry = 10) %>%
  set_engine("ranger")
mod_spec
## Random Forest Model Specification (classification)
## 
## Main Arguments:
##   mtry = 10
## 
## Computational engine: ranger
20 / 22
Dr. Lucy D'Agostino McGowan

Random Forest in R

mod_spec <- rand_forest(
  mode = "classification") %>%
  set_engine("ranger")
mod_spec
## Random Forest Model Specification (classification)
## 
## Computational engine: ranger
21 / 22
Dr. Lucy D'Agostino McGowan

Boosting in R

mod_spec <- boost_tree(
  mode = "classification") %>%
  set_engine("xgboost")
mod_spec
## Boosted Tree Model Specification (classification)
## 
## Computational engine: xgboost
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Dr. Lucy D'Agostino McGowan

What have we learned?

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